7  PBMCs: Determine Significantly Expanded Clones

7.1 Set up workspace

# Load libraries
library(dplyr)

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ forcats   1.0.0     ✔ readr     2.1.5
✔ ggplot2   3.5.1     ✔ stringr   1.5.1
✔ lubridate 1.9.4     ✔ tibble    3.2.1
✔ purrr     1.0.4     ✔ tidyr     1.3.1

── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(ggplot2)
library(patchwork)
library(ggprism)

# Load colors
pt_cols <- readRDS("Part0_Patient_Color.rds")
pt_fill <- readRDS("Part0_Patient_Fill.rds")

# Functions

# Create contingency table of reads
#                   TP1    TP2
# Clone1
# AllOtherClones

create_contingency_table <- function(betas_df, timepoint1, timepoint2, clone_i){
  # Get number of reads of clone at time point 1
  tp1_df <- betas_df[,c("Beta_clonotype", timepoint1)]
  clone_tp1_umi <- as.numeric(tp1_df[clone_i,2])
  # If the clone isn't present, set its value to 0
  if(is.na(clone_tp1_umi)){
    clone_tp1_umi <- 0
  }
  # Get number of reads of all other clones at time point 1
  allotherclones_tp1_umi <- sum(tp1_df[,2], na.rm = TRUE) - clone_tp1_umi
  
  # Get number of reads of clone at time point 2
  tp2_df <- betas_df[,c("Beta_clonotype", timepoint2)]
  clone_tp2_umi <- as.numeric(tp2_df[clone_i,2])
  # If the clone isn't present, set its value to 0
  if(is.na(clone_tp2_umi)){
    clone_tp2_umi <- 0
  }
  # Get number of reads of all other clones at time point 2
  allotherclones_tp2_umi <- sum(tp2_df[,2], na.rm = TRUE) - clone_tp2_umi
  
  contingency_table <- data.frame(
    "tp1" = c(clone_tp1_umi, allotherclones_tp1_umi),
    "tp2" = c(clone_tp2_umi, allotherclones_tp2_umi),
    row.names = c("clone1", "all_other_clones"),
    stringsAsFactors = FALSE
  )
  
  return(contingency_table)
}

run_fishers_test <- function(betas_df, timepoint_prevax, timepoint_postvax, test = "two.sided", correction = "fdr"){
  pval_df <- data.frame()
  for(i in c(1:nrow(betas_df))){
    # Print every 100 clones
    if(i %% 100 == 0){
      print(i)
    }
    contingency_i <- create_contingency_table(betas_df, timepoint_prevax, timepoint_postvax, i)
    # Fisher's exact test asking if pre-vax reads are less than post-vax reads
    pval <- fisher.test(contingency_i, alternative = test)$p.value
    # # Update direction if more reads in Prevax compared to Postvax
      # if(contingency_i[1,"tp1"] > contingency_i[1,"tp2"]) {
      #   direction <- "More_in_prevax"
      # }
      # # Update direction if more reads in Prevax compared to Postvax
      # if(contingency_i[1,"tp2"] > contingency_i[1,"tp1"]) {
      #   direction <- "More_in_postvax"
      # }
      # }
    # pval_df <- rbind(pval_df, c(pval, direction))
    pval_df <- rbind(pval_df, pval)
  }
  pval_df$Beta_clonotype <- betas_df$Beta_clonotype
  # colnames(pval_df) <- c("pval", "direction", "Beta_clonotype")
  colnames(pval_df) <- c("pval", "Beta_clonotype")
  
  pval_df <- pval_df %>%
    mutate(padj = p.adjust(pval, method = correction),
           sig = case_when(padj < 0.05 ~ "Sig",
                         padj >= 0.05 ~ "Not sig"))
  
  return(pval_df)
}

7.2 Load all clones

p101_betas <- read.csv("p101_betas_merged_Part1.csv")
p103_betas <- read.csv("p103_betas_merged_Part1.csv")
p104_betas <- read.csv("p104_betas_merged_Part1.csv")
p105_betas <- read.csv("p105_betas_merged_Part1.csv")
p106_betas <- read.csv("p106_betas_merged_Part1.csv")
p108_betas <- read.csv("p108_betas_merged_Part1.csv")
p109_betas <- read.csv("p109_betas_merged_Part1.csv")
p110_betas <- read.csv("p110_betas_merged_Part1.csv")
p111_betas <- read.csv("p111_betas_merged_Part1.csv")

7.3 Calculate number of clones significantly expanded post-vax compared to pre-vax

# Filter for clones with at least 3 reads post-vax or prevax
p101_betas_postvax_vs_prevax <- p101_betas %>%
  filter(p101_postvax_umi >= 3 | p101_prevax_umi >= 3) %>%
  ungroup()
p103_betas_postvax_vs_prevax <- p103_betas %>%
  filter(p103_postvax_umi >= 3 | p103_prevax_umi >= 3) %>%
  ungroup()
p104_betas_postvax_vs_prevax <- p104_betas %>%
  filter(p104_postvax_umi >= 3 | p104_prevax_umi >= 3) %>%
  ungroup()
p105_betas_postvax_vs_prevax <- p105_betas %>%
  filter(p105_postvax_umi >= 3 | p105_prevax_umi >= 3) %>%
  ungroup()
p106_betas_postvax_vs_prevax <- p106_betas %>%
  filter(p106_postvax_umi >= 3 | p106_prevax_umi >= 3) %>%
  ungroup()
p108_betas_postvax_vs_prevax <- p108_betas %>%
  filter(p108_postvax_umi >= 3 | p108_prevax_umi >= 3) %>%
  ungroup()
p109_betas_postvax_vs_prevax <- p109_betas %>%
  filter(p109_postvax_umi >= 3 | p109_prevax_umi >= 3) %>%
  ungroup()
p110_betas_postvax_vs_prevax <- p110_betas %>%
  filter(p110_postvax_umi >= 3 | p110_prevax_umi >= 3) %>%
  ungroup()
p111_betas_postvax_vs_prevax <- p111_betas %>%
  filter(p111_postvax_umi >= 3 | p111_prevax_umi >= 3) %>%
  ungroup()

# Calculate pval
p101_pval_postvax_vs_prevax <- run_fishers_test(p101_betas_postvax_vs_prevax, "p101_prevax_umi", "p101_postvax_umi", test = "less", correction = "BY")

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p103_pval_postvax_vs_prevax <- run_fishers_test(p103_betas_postvax_vs_prevax, "p103_prevax_umi", "p103_postvax_umi", test = "less", correction = "BY")

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p104_pval_postvax_vs_prevax <- run_fishers_test(p104_betas_postvax_vs_prevax, "p104_prevax_umi", "p104_postvax_umi", test = "less", correction = "BY")

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p105_pval_postvax_vs_prevax <- run_fishers_test(p105_betas_postvax_vs_prevax, "p105_prevax_umi", "p105_postvax_umi", test = "less", correction = "BY")

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[1] 20200
[1] 20300
[1] 20400
[1] 20500
[1] 20600
[1] 20700
[1] 20800
[1] 20900
[1] 21000
[1] 21100
[1] 21200
[1] 21300
[1] 21400
[1] 21500
[1] 21600
[1] 21700
[1] 21800
[1] 21900
[1] 22000
[1] 22100
[1] 22200
[1] 22300
[1] 22400
[1] 22500
[1] 22600

p106_pval_postvax_vs_prevax <- run_fishers_test(p106_betas_postvax_vs_prevax, "p106_prevax_umi", "p106_postvax_umi", test = "less", correction = "BY")

[1] 100
[1] 200
[1] 300
[1] 400
[1] 500
[1] 600
[1] 700
[1] 800
[1] 900
[1] 1000
[1] 1100
[1] 1200
[1] 1300
[1] 1400
[1] 1500
[1] 1600
[1] 1700
[1] 1800
[1] 1900
[1] 2000
[1] 2100
[1] 2200
[1] 2300
[1] 2400
[1] 2500
[1] 2600
[1] 2700
[1] 2800
[1] 2900
[1] 3000
[1] 3100
[1] 3200
[1] 3300
[1] 3400
[1] 3500
[1] 3600
[1] 3700
[1] 3800
[1] 3900
[1] 4000
[1] 4100
[1] 4200
[1] 4300
[1] 4400
[1] 4500
[1] 4600
[1] 4700
[1] 4800
[1] 4900
[1] 5000
[1] 5100
[1] 5200
[1] 5300
[1] 5400
[1] 5500
[1] 5600
[1] 5700
[1] 5800
[1] 5900
[1] 6000
[1] 6100
[1] 6200
[1] 6300
[1] 6400
[1] 6500
[1] 6600
[1] 6700
[1] 6800
[1] 6900
[1] 7000
[1] 7100
[1] 7200
[1] 7300
[1] 7400
[1] 7500
[1] 7600
[1] 7700
[1] 7800
[1] 7900
[1] 8000
[1] 8100
[1] 8200
[1] 8300
[1] 8400
[1] 8500
[1] 8600
[1] 8700
[1] 8800
[1] 8900
[1] 9000
[1] 9100
[1] 9200
[1] 9300
[1] 9400
[1] 9500
[1] 9600
[1] 9700
[1] 9800
[1] 9900
[1] 10000
[1] 10100
[1] 10200
[1] 10300
[1] 10400
[1] 10500
[1] 10600
[1] 10700
[1] 10800
[1] 10900
[1] 11000
[1] 11100
[1] 11200
[1] 11300
[1] 11400
[1] 11500
[1] 11600
[1] 11700
[1] 11800
[1] 11900
[1] 12000
[1] 12100
[1] 12200
[1] 12300
[1] 12400
[1] 12500
[1] 12600
[1] 12700
[1] 12800
[1] 12900
[1] 13000
[1] 13100
[1] 13200
[1] 13300
[1] 13400
[1] 13500
[1] 13600
[1] 13700
[1] 13800
[1] 13900
[1] 14000
[1] 14100
[1] 14200
[1] 14300
[1] 14400
[1] 14500
[1] 14600
[1] 14700
[1] 14800
[1] 14900
[1] 15000
[1] 15100
[1] 15200
[1] 15300
[1] 15400
[1] 15500
[1] 15600
[1] 15700
[1] 15800
[1] 15900
[1] 16000
[1] 16100
[1] 16200
[1] 16300
[1] 16400
[1] 16500
[1] 16600
[1] 16700
[1] 16800
[1] 16900
[1] 17000
[1] 17100
[1] 17200
[1] 17300
[1] 17400
[1] 17500
[1] 17600
[1] 17700
[1] 17800
[1] 17900
[1] 18000
[1] 18100
[1] 18200
[1] 18300
[1] 18400
[1] 18500
[1] 18600
[1] 18700
[1] 18800
[1] 18900
[1] 19000
[1] 19100
[1] 19200
[1] 19300
[1] 19400
[1] 19500
[1] 19600
[1] 19700
[1] 19800
[1] 19900
[1] 20000
[1] 20100
[1] 20200
[1] 20300
[1] 20400
[1] 20500
[1] 20600
[1] 20700
[1] 20800
[1] 20900
[1] 21000
[1] 21100
[1] 21200
[1] 21300
[1] 21400
[1] 21500
[1] 21600
[1] 21700
[1] 21800
[1] 21900
[1] 22000
[1] 22100
[1] 22200
[1] 22300
[1] 22400
[1] 22500
[1] 22600
[1] 22700
[1] 22800
[1] 22900
[1] 23000
[1] 23100
[1] 23200
[1] 23300
[1] 23400
[1] 23500
[1] 23600
[1] 23700
[1] 23800
[1] 23900
[1] 24000
[1] 24100
[1] 24200
[1] 24300
[1] 24400
[1] 24500
[1] 24600
[1] 24700
[1] 24800
[1] 24900
[1] 25000
[1] 25100
[1] 25200
[1] 25300
[1] 25400

p108_pval_postvax_vs_prevax <- run_fishers_test(p108_betas_postvax_vs_prevax, "p108_prevax_umi", "p108_postvax_umi", test = "less", correction = "BY")

[1] 100
[1] 200
[1] 300
[1] 400
[1] 500
[1] 600
[1] 700
[1] 800
[1] 900
[1] 1000
[1] 1100
[1] 1200
[1] 1300
[1] 1400
[1] 1500
[1] 1600
[1] 1700
[1] 1800
[1] 1900
[1] 2000
[1] 2100
[1] 2200
[1] 2300
[1] 2400
[1] 2500
[1] 2600
[1] 2700
[1] 2800
[1] 2900
[1] 3000
[1] 3100
[1] 3200
[1] 3300
[1] 3400
[1] 3500
[1] 3600
[1] 3700
[1] 3800
[1] 3900
[1] 4000
[1] 4100
[1] 4200
[1] 4300
[1] 4400
[1] 4500
[1] 4600
[1] 4700
[1] 4800
[1] 4900
[1] 5000
[1] 5100
[1] 5200
[1] 5300
[1] 5400
[1] 5500
[1] 5600
[1] 5700
[1] 5800
[1] 5900
[1] 6000
[1] 6100
[1] 6200
[1] 6300
[1] 6400
[1] 6500
[1] 6600
[1] 6700
[1] 6800
[1] 6900
[1] 7000
[1] 7100
[1] 7200
[1] 7300
[1] 7400
[1] 7500
[1] 7600
[1] 7700
[1] 7800
[1] 7900
[1] 8000
[1] 8100
[1] 8200
[1] 8300
[1] 8400
[1] 8500
[1] 8600
[1] 8700
[1] 8800
[1] 8900
[1] 9000
[1] 9100
[1] 9200
[1] 9300
[1] 9400
[1] 9500
[1] 9600
[1] 9700
[1] 9800
[1] 9900
[1] 10000
[1] 10100
[1] 10200
[1] 10300
[1] 10400
[1] 10500
[1] 10600
[1] 10700
[1] 10800
[1] 10900
[1] 11000
[1] 11100
[1] 11200
[1] 11300
[1] 11400
[1] 11500
[1] 11600
[1] 11700
[1] 11800
[1] 11900
[1] 12000
[1] 12100
[1] 12200
[1] 12300
[1] 12400
[1] 12500
[1] 12600
[1] 12700
[1] 12800
[1] 12900
[1] 13000
[1] 13100
[1] 13200
[1] 13300
[1] 13400
[1] 13500
[1] 13600
[1] 13700
[1] 13800
[1] 13900
[1] 14000
[1] 14100
[1] 14200
[1] 14300
[1] 14400
[1] 14500
[1] 14600
[1] 14700
[1] 14800
[1] 14900
[1] 15000
[1] 15100
[1] 15200
[1] 15300
[1] 15400
[1] 15500
[1] 15600
[1] 15700
[1] 15800
[1] 15900
[1] 16000
[1] 16100
[1] 16200
[1] 16300
[1] 16400
[1] 16500
[1] 16600
[1] 16700
[1] 16800
[1] 16900
[1] 17000
[1] 17100
[1] 17200
[1] 17300
[1] 17400
[1] 17500
[1] 17600
[1] 17700
[1] 17800
[1] 17900
[1] 18000
[1] 18100
[1] 18200
[1] 18300
[1] 18400
[1] 18500
[1] 18600
[1] 18700
[1] 18800
[1] 18900
[1] 19000
[1] 19100
[1] 19200
[1] 19300
[1] 19400
[1] 19500
[1] 19600
[1] 19700
[1] 19800
[1] 19900
[1] 20000
[1] 20100
[1] 20200
[1] 20300
[1] 20400
[1] 20500
[1] 20600
[1] 20700
[1] 20800
[1] 20900
[1] 21000
[1] 21100
[1] 21200
[1] 21300
[1] 21400
[1] 21500
[1] 21600
[1] 21700
[1] 21800
[1] 21900
[1] 22000
[1] 22100
[1] 22200
[1] 22300
[1] 22400
[1] 22500
[1] 22600
[1] 22700
[1] 22800
[1] 22900
[1] 23000
[1] 23100
[1] 23200
[1] 23300
[1] 23400
[1] 23500
[1] 23600
[1] 23700
[1] 23800
[1] 23900
[1] 24000
[1] 24100
[1] 24200
[1] 24300
[1] 24400
[1] 24500
[1] 24600
[1] 24700
[1] 24800
[1] 24900
[1] 25000
[1] 25100
[1] 25200
[1] 25300
[1] 25400
[1] 25500
[1] 25600
[1] 25700
[1] 25800
[1] 25900
[1] 26000
[1] 26100
[1] 26200
[1] 26300
[1] 26400
[1] 26500
[1] 26600
[1] 26700
[1] 26800
[1] 26900
[1] 27000
[1] 27100
[1] 27200
[1] 27300
[1] 27400
[1] 27500
[1] 27600
[1] 27700
[1] 27800
[1] 27900
[1] 28000
[1] 28100
[1] 28200
[1] 28300
[1] 28400
[1] 28500
[1] 28600
[1] 28700
[1] 28800
[1] 28900
[1] 29000
[1] 29100

p109_pval_postvax_vs_prevax <- run_fishers_test(p109_betas_postvax_vs_prevax, "p109_prevax_umi", "p109_postvax_umi", test = "less", correction = "BY")

[1] 100
[1] 200
[1] 300
[1] 400
[1] 500
[1] 600
[1] 700
[1] 800
[1] 900
[1] 1000
[1] 1100
[1] 1200
[1] 1300
[1] 1400
[1] 1500
[1] 1600
[1] 1700
[1] 1800
[1] 1900
[1] 2000
[1] 2100
[1] 2200
[1] 2300
[1] 2400
[1] 2500
[1] 2600
[1] 2700
[1] 2800
[1] 2900
[1] 3000
[1] 3100
[1] 3200
[1] 3300
[1] 3400
[1] 3500
[1] 3600
[1] 3700
[1] 3800
[1] 3900
[1] 4000
[1] 4100
[1] 4200
[1] 4300
[1] 4400
[1] 4500
[1] 4600
[1] 4700
[1] 4800
[1] 4900
[1] 5000
[1] 5100
[1] 5200
[1] 5300
[1] 5400
[1] 5500
[1] 5600
[1] 5700
[1] 5800
[1] 5900
[1] 6000
[1] 6100
[1] 6200
[1] 6300
[1] 6400
[1] 6500
[1] 6600
[1] 6700
[1] 6800
[1] 6900
[1] 7000
[1] 7100
[1] 7200
[1] 7300
[1] 7400
[1] 7500
[1] 7600
[1] 7700
[1] 7800
[1] 7900
[1] 8000
[1] 8100
[1] 8200
[1] 8300
[1] 8400
[1] 8500
[1] 8600
[1] 8700
[1] 8800
[1] 8900
[1] 9000
[1] 9100
[1] 9200
[1] 9300
[1] 9400
[1] 9500
[1] 9600
[1] 9700
[1] 9800
[1] 9900
[1] 10000
[1] 10100
[1] 10200
[1] 10300
[1] 10400
[1] 10500
[1] 10600
[1] 10700
[1] 10800
[1] 10900
[1] 11000
[1] 11100
[1] 11200
[1] 11300
[1] 11400
[1] 11500
[1] 11600
[1] 11700
[1] 11800
[1] 11900
[1] 12000
[1] 12100
[1] 12200
[1] 12300
[1] 12400
[1] 12500
[1] 12600
[1] 12700
[1] 12800
[1] 12900
[1] 13000
[1] 13100
[1] 13200
[1] 13300
[1] 13400
[1] 13500
[1] 13600
[1] 13700
[1] 13800

p110_pval_postvax_vs_prevax <- run_fishers_test(p110_betas_postvax_vs_prevax, "p110_prevax_umi", "p110_postvax_umi", test = "less", correction = "BY")

[1] 100
[1] 200
[1] 300
[1] 400
[1] 500
[1] 600
[1] 700
[1] 800
[1] 900
[1] 1000
[1] 1100
[1] 1200
[1] 1300
[1] 1400
[1] 1500
[1] 1600
[1] 1700
[1] 1800
[1] 1900
[1] 2000
[1] 2100
[1] 2200
[1] 2300
[1] 2400
[1] 2500
[1] 2600
[1] 2700
[1] 2800
[1] 2900
[1] 3000
[1] 3100
[1] 3200
[1] 3300
[1] 3400
[1] 3500
[1] 3600
[1] 3700
[1] 3800
[1] 3900
[1] 4000
[1] 4100
[1] 4200
[1] 4300
[1] 4400
[1] 4500
[1] 4600
[1] 4700
[1] 4800
[1] 4900
[1] 5000
[1] 5100
[1] 5200
[1] 5300
[1] 5400
[1] 5500
[1] 5600
[1] 5700
[1] 5800
[1] 5900
[1] 6000
[1] 6100
[1] 6200
[1] 6300
[1] 6400
[1] 6500
[1] 6600
[1] 6700
[1] 6800
[1] 6900
[1] 7000
[1] 7100
[1] 7200
[1] 7300
[1] 7400
[1] 7500
[1] 7600
[1] 7700
[1] 7800
[1] 7900
[1] 8000
[1] 8100
[1] 8200
[1] 8300
[1] 8400
[1] 8500
[1] 8600
[1] 8700
[1] 8800
[1] 8900
[1] 9000
[1] 9100
[1] 9200
[1] 9300
[1] 9400
[1] 9500
[1] 9600
[1] 9700
[1] 9800
[1] 9900
[1] 10000
[1] 10100
[1] 10200
[1] 10300
[1] 10400
[1] 10500
[1] 10600
[1] 10700
[1] 10800
[1] 10900
[1] 11000
[1] 11100
[1] 11200
[1] 11300
[1] 11400
[1] 11500
[1] 11600
[1] 11700
[1] 11800
[1] 11900
[1] 12000
[1] 12100
[1] 12200
[1] 12300
[1] 12400
[1] 12500
[1] 12600
[1] 12700
[1] 12800
[1] 12900
[1] 13000
[1] 13100
[1] 13200
[1] 13300
[1] 13400
[1] 13500
[1] 13600
[1] 13700
[1] 13800
[1] 13900
[1] 14000
[1] 14100
[1] 14200
[1] 14300
[1] 14400
[1] 14500
[1] 14600
[1] 14700
[1] 14800
[1] 14900
[1] 15000
[1] 15100
[1] 15200
[1] 15300
[1] 15400
[1] 15500
[1] 15600
[1] 15700
[1] 15800
[1] 15900
[1] 16000
[1] 16100
[1] 16200
[1] 16300
[1] 16400
[1] 16500
[1] 16600
[1] 16700
[1] 16800
[1] 16900
[1] 17000
[1] 17100
[1] 17200
[1] 17300
[1] 17400
[1] 17500
[1] 17600
[1] 17700
[1] 17800
[1] 17900
[1] 18000
[1] 18100
[1] 18200
[1] 18300
[1] 18400
[1] 18500
[1] 18600
[1] 18700
[1] 18800
[1] 18900
[1] 19000
[1] 19100
[1] 19200
[1] 19300
[1] 19400
[1] 19500
[1] 19600
[1] 19700
[1] 19800
[1] 19900
[1] 20000
[1] 20100
[1] 20200
[1] 20300
[1] 20400
[1] 20500
[1] 20600
[1] 20700
[1] 20800
[1] 20900
[1] 21000
[1] 21100
[1] 21200
[1] 21300
[1] 21400
[1] 21500
[1] 21600
[1] 21700
[1] 21800
[1] 21900
[1] 22000
[1] 22100
[1] 22200
[1] 22300
[1] 22400
[1] 22500
[1] 22600
[1] 22700
[1] 22800
[1] 22900
[1] 23000
[1] 23100
[1] 23200
[1] 23300
[1] 23400
[1] 23500
[1] 23600
[1] 23700
[1] 23800
[1] 23900
[1] 24000
[1] 24100
[1] 24200
[1] 24300
[1] 24400
[1] 24500
[1] 24600
[1] 24700
[1] 24800
[1] 24900
[1] 25000
[1] 25100
[1] 25200
[1] 25300
[1] 25400
[1] 25500
[1] 25600
[1] 25700
[1] 25800
[1] 25900
[1] 26000
[1] 26100
[1] 26200
[1] 26300
[1] 26400
[1] 26500
[1] 26600
[1] 26700
[1] 26800
[1] 26900
[1] 27000
[1] 27100
[1] 27200
[1] 27300
[1] 27400
[1] 27500
[1] 27600
[1] 27700
[1] 27800
[1] 27900
[1] 28000
[1] 28100
[1] 28200
[1] 28300
[1] 28400
[1] 28500
[1] 28600
[1] 28700
[1] 28800
[1] 28900
[1] 29000
[1] 29100
[1] 29200
[1] 29300
[1] 29400
[1] 29500
[1] 29600
[1] 29700
[1] 29800
[1] 29900
[1] 30000
[1] 30100
[1] 30200
[1] 30300
[1] 30400
[1] 30500
[1] 30600
[1] 30700
[1] 30800
[1] 30900

p111_pval_postvax_vs_prevax <- run_fishers_test(p111_betas_postvax_vs_prevax, "p111_prevax_umi", "p111_postvax_umi", test = "less", correction = "BY")

[1] 100
[1] 200
[1] 300
[1] 400
[1] 500
[1] 600
[1] 700
[1] 800
[1] 900
[1] 1000
[1] 1100
[1] 1200
[1] 1300
[1] 1400
[1] 1500
[1] 1600
[1] 1700
[1] 1800
[1] 1900
[1] 2000
[1] 2100
[1] 2200
[1] 2300
[1] 2400
[1] 2500
[1] 2600
[1] 2700
[1] 2800
[1] 2900
[1] 3000
[1] 3100
[1] 3200
[1] 3300
[1] 3400
[1] 3500
[1] 3600
[1] 3700
[1] 3800
[1] 3900
[1] 4000
[1] 4100
[1] 4200
[1] 4300
[1] 4400
[1] 4500
[1] 4600
[1] 4700
[1] 4800
[1] 4900
[1] 5000
[1] 5100
[1] 5200
[1] 5300
[1] 5400
[1] 5500
[1] 5600
[1] 5700
[1] 5800
[1] 5900
[1] 6000
[1] 6100
[1] 6200
[1] 6300
[1] 6400
[1] 6500
[1] 6600
[1] 6700
[1] 6800
[1] 6900
[1] 7000
[1] 7100
[1] 7200
[1] 7300
[1] 7400
[1] 7500
[1] 7600
[1] 7700
[1] 7800
[1] 7900
[1] 8000
[1] 8100
[1] 8200
[1] 8300
[1] 8400
[1] 8500
[1] 8600
[1] 8700
[1] 8800
[1] 8900
[1] 9000
[1] 9100
[1] 9200
[1] 9300
[1] 9400
[1] 9500
[1] 9600
[1] 9700
[1] 9800
[1] 9900
[1] 10000
[1] 10100
[1] 10200
[1] 10300
[1] 10400
[1] 10500
[1] 10600
[1] 10700
[1] 10800
[1] 10900
[1] 11000
[1] 11100
[1] 11200
[1] 11300
[1] 11400
[1] 11500
[1] 11600
[1] 11700
[1] 11800
[1] 11900
[1] 12000
[1] 12100
[1] 12200
[1] 12300
[1] 12400
[1] 12500
[1] 12600
[1] 12700
[1] 12800
[1] 12900
[1] 13000
[1] 13100
[1] 13200
[1] 13300
[1] 13400
[1] 13500
[1] 13600
[1] 13700
[1] 13800
[1] 13900
[1] 14000
[1] 14100
[1] 14200
[1] 14300
[1] 14400
[1] 14500
[1] 14600
[1] 14700
[1] 14800
[1] 14900
[1] 15000
[1] 15100
[1] 15200
[1] 15300
[1] 15400
[1] 15500
[1] 15600
[1] 15700
[1] 15800
[1] 15900
[1] 16000
[1] 16100
[1] 16200
[1] 16300
[1] 16400
[1] 16500
[1] 16600
[1] 16700
[1] 16800
[1] 16900
[1] 17000
[1] 17100
[1] 17200
[1] 17300
[1] 17400
[1] 17500
[1] 17600
[1] 17700
[1] 17800
[1] 17900
[1] 18000
[1] 18100
[1] 18200
[1] 18300
[1] 18400
[1] 18500
[1] 18600
[1] 18700
[1] 18800
[1] 18900
[1] 19000
[1] 19100
[1] 19200
[1] 19300
[1] 19400
[1] 19500
[1] 19600
[1] 19700
[1] 19800
[1] 19900
[1] 20000
[1] 20100
[1] 20200
[1] 20300
[1] 20400
[1] 20500
[1] 20600
[1] 20700
[1] 20800
[1] 20900
[1] 21000
[1] 21100
[1] 21200
[1] 21300
[1] 21400
[1] 21500
[1] 21600
[1] 21700
[1] 21800
[1] 21900
[1] 22000
[1] 22100
[1] 22200
[1] 22300
[1] 22400
[1] 22500
[1] 22600
[1] 22700
[1] 22800
[1] 22900
[1] 23000
[1] 23100
[1] 23200

7.4 Count how many clones are significantly expanded post-vax

p101_pval_postvax_vs_prevax <- p101_pval_postvax_vs_prevax %>%
  mutate(Patient = "P101")
p103_pval_postvax_vs_prevax <- p103_pval_postvax_vs_prevax %>%
  mutate(Patient = "P103")
p104_pval_postvax_vs_prevax <- p104_pval_postvax_vs_prevax %>%
  mutate(Patient = "P104")
p105_pval_postvax_vs_prevax <- p105_pval_postvax_vs_prevax %>%
  mutate(Patient = "P105")
p106_pval_postvax_vs_prevax <- p106_pval_postvax_vs_prevax %>%
  mutate(Patient = "P106")
p108_pval_postvax_vs_prevax <- p108_pval_postvax_vs_prevax %>%
  mutate(Patient = "P108")
p109_pval_postvax_vs_prevax <- p109_pval_postvax_vs_prevax %>%
  mutate(Patient = "P109")
p110_pval_postvax_vs_prevax <- p110_pval_postvax_vs_prevax %>%
  mutate(Patient = "P110")
p111_pval_postvax_vs_prevax <- p111_pval_postvax_vs_prevax %>%
  mutate(Patient = "P111")

pval_postvax_vs_prevax <- do.call(rbind, list(p101_pval_postvax_vs_prevax,
                                              p103_pval_postvax_vs_prevax,
                                              p104_pval_postvax_vs_prevax,
                                              p105_pval_postvax_vs_prevax,
                                              p106_pval_postvax_vs_prevax,
                                              p108_pval_postvax_vs_prevax,
                                              p109_pval_postvax_vs_prevax,
                                              p110_pval_postvax_vs_prevax,
                                              p111_pval_postvax_vs_prevax))

7.5 Calculate number of clones significantly expanded post-nivo compared to pre-nivo

# Filter for clones with at least 3 reads in prevax or pretreatment
p101_betas_prevax_vs_pretreatment <- p101_betas %>%
  filter(p101_pretreatment_umi >= 3 | p101_prevax_umi >= 3) %>%
  ungroup()
p103_betas_prevax_vs_pretreatment <- p103_betas %>%
  filter(p103_pretreatment_umi >= 3 | p103_prevax_umi >= 3) %>%
  ungroup()
p104_betas_prevax_vs_pretreatment <- p104_betas %>%
  filter(p104_pretreatment_umi >= 3 | p104_prevax_umi >= 3) %>%
  ungroup()
p105_betas_prevax_vs_pretreatment <- p105_betas %>%
  filter(p105_pretreatment_umi >= 3 | p105_prevax_umi >= 3) %>%
  ungroup()
p106_betas_prevax_vs_pretreatment <- p106_betas %>%
  filter(p106_pretreatment_umi >= 3 | p106_prevax_umi >= 3) %>%
  ungroup()
p108_betas_prevax_vs_pretreatment <- p108_betas %>%
  filter(p108_pretreatment_umi >= 3 | p108_prevax_umi >= 3) %>%
  ungroup()
p109_betas_prevax_vs_pretreatment <- p109_betas %>%
  filter(p109_pretreatment_umi >= 3 | p109_prevax_umi >= 3) %>%
  ungroup()
p110_betas_prevax_vs_pretreatment <- p110_betas %>%
  filter(p110_pretreatment_umi >= 3 | p110_prevax_umi >= 3) %>%
  ungroup()
p111_betas_prevax_vs_pretreatment <- p111_betas %>%
  filter(p111_pretreatment_umi >= 3 | p111_prevax_umi >= 3) %>%
  ungroup()

# Calculate pval
p101_pval_prevax_vs_pretreatment <- run_fishers_test(p101_betas_prevax_vs_pretreatment, "p101_pretreatment_umi", "p101_prevax_umi", test = "less", correction = "BY")

[1] 100
[1] 200
[1] 300
[1] 400
[1] 500
[1] 600
[1] 700
[1] 800
[1] 900
[1] 1000
[1] 1100
[1] 1200
[1] 1300
[1] 1400
[1] 1500
[1] 1600
[1] 1700
[1] 1800
[1] 1900
[1] 2000
[1] 2100
[1] 2200
[1] 2300
[1] 2400
[1] 2500
[1] 2600
[1] 2700
[1] 2800
[1] 2900
[1] 3000
[1] 3100
[1] 3200
[1] 3300
[1] 3400
[1] 3500
[1] 3600
[1] 3700
[1] 3800
[1] 3900
[1] 4000
[1] 4100
[1] 4200
[1] 4300
[1] 4400
[1] 4500
[1] 4600
[1] 4700
[1] 4800
[1] 4900
[1] 5000
[1] 5100
[1] 5200
[1] 5300
[1] 5400
[1] 5500
[1] 5600
[1] 5700
[1] 5800
[1] 5900
[1] 6000
[1] 6100
[1] 6200
[1] 6300
[1] 6400
[1] 6500
[1] 6600
[1] 6700
[1] 6800
[1] 6900
[1] 7000
[1] 7100
[1] 7200
[1] 7300
[1] 7400
[1] 7500
[1] 7600
[1] 7700
[1] 7800
[1] 7900
[1] 8000
[1] 8100
[1] 8200
[1] 8300
[1] 8400
[1] 8500
[1] 8600
[1] 8700
[1] 8800
[1] 8900
[1] 9000
[1] 9100
[1] 9200
[1] 9300
[1] 9400
[1] 9500
[1] 9600
[1] 9700
[1] 9800
[1] 9900
[1] 10000
[1] 10100
[1] 10200
[1] 10300
[1] 10400
[1] 10500
[1] 10600
[1] 10700
[1] 10800
[1] 10900
[1] 11000
[1] 11100
[1] 11200
[1] 11300
[1] 11400
[1] 11500
[1] 11600
[1] 11700
[1] 11800
[1] 11900
[1] 12000
[1] 12100
[1] 12200
[1] 12300
[1] 12400
[1] 12500
[1] 12600
[1] 12700
[1] 12800
[1] 12900
[1] 13000
[1] 13100
[1] 13200
[1] 13300
[1] 13400
[1] 13500

p103_pval_prevax_vs_pretreatment <- run_fishers_test(p103_betas_prevax_vs_pretreatment, "p103_pretreatment_umi", "p103_prevax_umi", test = "less", correction = "BY")

[1] 100
[1] 200
[1] 300
[1] 400
[1] 500
[1] 600
[1] 700
[1] 800
[1] 900
[1] 1000
[1] 1100
[1] 1200
[1] 1300
[1] 1400
[1] 1500
[1] 1600
[1] 1700
[1] 1800
[1] 1900
[1] 2000
[1] 2100
[1] 2200
[1] 2300
[1] 2400
[1] 2500
[1] 2600
[1] 2700
[1] 2800
[1] 2900
[1] 3000
[1] 3100
[1] 3200
[1] 3300
[1] 3400
[1] 3500
[1] 3600
[1] 3700
[1] 3800
[1] 3900
[1] 4000
[1] 4100
[1] 4200
[1] 4300
[1] 4400
[1] 4500
[1] 4600
[1] 4700
[1] 4800
[1] 4900
[1] 5000
[1] 5100
[1] 5200
[1] 5300
[1] 5400
[1] 5500
[1] 5600
[1] 5700
[1] 5800
[1] 5900
[1] 6000
[1] 6100
[1] 6200
[1] 6300
[1] 6400
[1] 6500
[1] 6600
[1] 6700
[1] 6800
[1] 6900
[1] 7000
[1] 7100
[1] 7200
[1] 7300
[1] 7400
[1] 7500
[1] 7600
[1] 7700
[1] 7800
[1] 7900
[1] 8000
[1] 8100
[1] 8200
[1] 8300
[1] 8400
[1] 8500
[1] 8600
[1] 8700
[1] 8800
[1] 8900
[1] 9000
[1] 9100
[1] 9200
[1] 9300
[1] 9400
[1] 9500
[1] 9600
[1] 9700
[1] 9800
[1] 9900
[1] 10000
[1] 10100
[1] 10200
[1] 10300
[1] 10400
[1] 10500
[1] 10600
[1] 10700
[1] 10800
[1] 10900
[1] 11000
[1] 11100
[1] 11200
[1] 11300
[1] 11400
[1] 11500
[1] 11600
[1] 11700
[1] 11800
[1] 11900
[1] 12000
[1] 12100
[1] 12200
[1] 12300
[1] 12400
[1] 12500
[1] 12600
[1] 12700
[1] 12800
[1] 12900
[1] 13000
[1] 13100
[1] 13200
[1] 13300
[1] 13400
[1] 13500
[1] 13600
[1] 13700
[1] 13800
[1] 13900
[1] 14000
[1] 14100
[1] 14200
[1] 14300
[1] 14400
[1] 14500
[1] 14600
[1] 14700
[1] 14800
[1] 14900
[1] 15000
[1] 15100
[1] 15200
[1] 15300
[1] 15400
[1] 15500
[1] 15600
[1] 15700
[1] 15800
[1] 15900
[1] 16000
[1] 16100
[1] 16200
[1] 16300
[1] 16400
[1] 16500
[1] 16600
[1] 16700
[1] 16800
[1] 16900
[1] 17000
[1] 17100
[1] 17200
[1] 17300
[1] 17400
[1] 17500
[1] 17600
[1] 17700
[1] 17800
[1] 17900
[1] 18000
[1] 18100
[1] 18200
[1] 18300
[1] 18400
[1] 18500
[1] 18600
[1] 18700
[1] 18800
[1] 18900
[1] 19000
[1] 19100
[1] 19200
[1] 19300
[1] 19400
[1] 19500
[1] 19600
[1] 19700
[1] 19800
[1] 19900
[1] 20000
[1] 20100
[1] 20200
[1] 20300
[1] 20400
[1] 20500
[1] 20600
[1] 20700
[1] 20800

p104_pval_prevax_vs_pretreatment <- run_fishers_test(p104_betas_prevax_vs_pretreatment, "p104_pretreatment_umi", "p104_prevax_umi", test = "less", correction = "BY")

[1] 100
[1] 200
[1] 300
[1] 400
[1] 500
[1] 600
[1] 700
[1] 800
[1] 900
[1] 1000
[1] 1100
[1] 1200
[1] 1300
[1] 1400
[1] 1500
[1] 1600
[1] 1700
[1] 1800
[1] 1900
[1] 2000
[1] 2100
[1] 2200
[1] 2300
[1] 2400
[1] 2500
[1] 2600
[1] 2700
[1] 2800
[1] 2900
[1] 3000
[1] 3100
[1] 3200
[1] 3300
[1] 3400
[1] 3500
[1] 3600
[1] 3700
[1] 3800
[1] 3900
[1] 4000
[1] 4100
[1] 4200
[1] 4300
[1] 4400
[1] 4500
[1] 4600
[1] 4700
[1] 4800
[1] 4900
[1] 5000
[1] 5100
[1] 5200
[1] 5300
[1] 5400
[1] 5500
[1] 5600
[1] 5700
[1] 5800
[1] 5900
[1] 6000
[1] 6100
[1] 6200
[1] 6300
[1] 6400
[1] 6500
[1] 6600
[1] 6700
[1] 6800
[1] 6900
[1] 7000
[1] 7100
[1] 7200
[1] 7300
[1] 7400
[1] 7500
[1] 7600
[1] 7700
[1] 7800
[1] 7900
[1] 8000
[1] 8100
[1] 8200
[1] 8300
[1] 8400
[1] 8500
[1] 8600
[1] 8700
[1] 8800
[1] 8900
[1] 9000
[1] 9100
[1] 9200
[1] 9300
[1] 9400
[1] 9500
[1] 9600
[1] 9700
[1] 9800
[1] 9900
[1] 10000
[1] 10100
[1] 10200
[1] 10300
[1] 10400
[1] 10500
[1] 10600
[1] 10700
[1] 10800
[1] 10900
[1] 11000
[1] 11100
[1] 11200
[1] 11300
[1] 11400
[1] 11500
[1] 11600
[1] 11700
[1] 11800
[1] 11900
[1] 12000
[1] 12100
[1] 12200
[1] 12300
[1] 12400
[1] 12500
[1] 12600
[1] 12700
[1] 12800
[1] 12900
[1] 13000
[1] 13100
[1] 13200
[1] 13300
[1] 13400
[1] 13500
[1] 13600
[1] 13700
[1] 13800
[1] 13900
[1] 14000
[1] 14100
[1] 14200
[1] 14300
[1] 14400
[1] 14500
[1] 14600
[1] 14700
[1] 14800
[1] 14900
[1] 15000
[1] 15100
[1] 15200
[1] 15300
[1] 15400
[1] 15500
[1] 15600
[1] 15700
[1] 15800
[1] 15900
[1] 16000
[1] 16100
[1] 16200
[1] 16300
[1] 16400
[1] 16500
[1] 16600
[1] 16700
[1] 16800
[1] 16900
[1] 17000
[1] 17100
[1] 17200
[1] 17300
[1] 17400
[1] 17500
[1] 17600
[1] 17700
[1] 17800
[1] 17900
[1] 18000
[1] 18100
[1] 18200
[1] 18300
[1] 18400
[1] 18500
[1] 18600
[1] 18700
[1] 18800
[1] 18900
[1] 19000
[1] 19100
[1] 19200
[1] 19300
[1] 19400
[1] 19500
[1] 19600
[1] 19700
[1] 19800
[1] 19900
[1] 20000
[1] 20100
[1] 20200
[1] 20300
[1] 20400
[1] 20500
[1] 20600
[1] 20700
[1] 20800
[1] 20900
[1] 21000
[1] 21100
[1] 21200
[1] 21300
[1] 21400
[1] 21500
[1] 21600
[1] 21700
[1] 21800
[1] 21900
[1] 22000
[1] 22100
[1] 22200
[1] 22300
[1] 22400
[1] 22500
[1] 22600
[1] 22700
[1] 22800
[1] 22900
[1] 23000
[1] 23100
[1] 23200
[1] 23300
[1] 23400
[1] 23500
[1] 23600
[1] 23700
[1] 23800
[1] 23900
[1] 24000
[1] 24100
[1] 24200
[1] 24300
[1] 24400
[1] 24500
[1] 24600
[1] 24700
[1] 24800
[1] 24900
[1] 25000
[1] 25100
[1] 25200
[1] 25300
[1] 25400
[1] 25500
[1] 25600
[1] 25700
[1] 25800
[1] 25900
[1] 26000
[1] 26100
[1] 26200
[1] 26300
[1] 26400
[1] 26500
[1] 26600
[1] 26700
[1] 26800
[1] 26900
[1] 27000
[1] 27100
[1] 27200
[1] 27300
[1] 27400
[1] 27500
[1] 27600
[1] 27700
[1] 27800
[1] 27900
[1] 28000
[1] 28100
[1] 28200
[1] 28300
[1] 28400
[1] 28500

p105_pval_prevax_vs_pretreatment <- run_fishers_test(p105_betas_prevax_vs_pretreatment, "p105_pretreatment_umi", "p105_prevax_umi", test = "less", correction = "BY")

[1] 100
[1] 200
[1] 300
[1] 400
[1] 500
[1] 600
[1] 700
[1] 800
[1] 900
[1] 1000
[1] 1100
[1] 1200
[1] 1300
[1] 1400
[1] 1500
[1] 1600
[1] 1700
[1] 1800
[1] 1900
[1] 2000
[1] 2100
[1] 2200
[1] 2300
[1] 2400
[1] 2500
[1] 2600
[1] 2700
[1] 2800
[1] 2900
[1] 3000
[1] 3100
[1] 3200
[1] 3300
[1] 3400
[1] 3500
[1] 3600
[1] 3700
[1] 3800
[1] 3900
[1] 4000
[1] 4100
[1] 4200
[1] 4300
[1] 4400
[1] 4500
[1] 4600
[1] 4700
[1] 4800
[1] 4900
[1] 5000
[1] 5100
[1] 5200
[1] 5300
[1] 5400
[1] 5500
[1] 5600
[1] 5700
[1] 5800
[1] 5900
[1] 6000
[1] 6100
[1] 6200
[1] 6300
[1] 6400
[1] 6500
[1] 6600
[1] 6700
[1] 6800
[1] 6900
[1] 7000
[1] 7100
[1] 7200
[1] 7300
[1] 7400
[1] 7500
[1] 7600
[1] 7700
[1] 7800
[1] 7900
[1] 8000
[1] 8100
[1] 8200
[1] 8300
[1] 8400
[1] 8500
[1] 8600
[1] 8700
[1] 8800
[1] 8900
[1] 9000
[1] 9100
[1] 9200
[1] 9300
[1] 9400
[1] 9500
[1] 9600
[1] 9700
[1] 9800
[1] 9900
[1] 10000
[1] 10100
[1] 10200
[1] 10300
[1] 10400
[1] 10500
[1] 10600
[1] 10700
[1] 10800
[1] 10900
[1] 11000
[1] 11100
[1] 11200
[1] 11300
[1] 11400
[1] 11500
[1] 11600
[1] 11700
[1] 11800
[1] 11900
[1] 12000
[1] 12100
[1] 12200
[1] 12300
[1] 12400
[1] 12500
[1] 12600
[1] 12700
[1] 12800
[1] 12900
[1] 13000
[1] 13100
[1] 13200
[1] 13300
[1] 13400
[1] 13500
[1] 13600
[1] 13700
[1] 13800
[1] 13900
[1] 14000
[1] 14100
[1] 14200
[1] 14300
[1] 14400
[1] 14500
[1] 14600
[1] 14700
[1] 14800
[1] 14900
[1] 15000
[1] 15100
[1] 15200
[1] 15300
[1] 15400
[1] 15500
[1] 15600
[1] 15700
[1] 15800
[1] 15900
[1] 16000
[1] 16100
[1] 16200
[1] 16300
[1] 16400
[1] 16500
[1] 16600
[1] 16700
[1] 16800
[1] 16900
[1] 17000
[1] 17100
[1] 17200
[1] 17300
[1] 17400
[1] 17500
[1] 17600
[1] 17700
[1] 17800
[1] 17900
[1] 18000
[1] 18100
[1] 18200
[1] 18300
[1] 18400
[1] 18500
[1] 18600
[1] 18700
[1] 18800
[1] 18900
[1] 19000
[1] 19100
[1] 19200
[1] 19300
[1] 19400
[1] 19500
[1] 19600
[1] 19700
[1] 19800
[1] 19900
[1] 20000
[1] 20100
[1] 20200
[1] 20300
[1] 20400
[1] 20500
[1] 20600
[1] 20700
[1] 20800
[1] 20900
[1] 21000
[1] 21100
[1] 21200
[1] 21300
[1] 21400
[1] 21500
[1] 21600
[1] 21700
[1] 21800
[1] 21900
[1] 22000
[1] 22100
[1] 22200
[1] 22300
[1] 22400
[1] 22500
[1] 22600
[1] 22700
[1] 22800
[1] 22900
[1] 23000
[1] 23100
[1] 23200
[1] 23300
[1] 23400
[1] 23500
[1] 23600
[1] 23700
[1] 23800
[1] 23900

p106_pval_prevax_vs_pretreatment <- run_fishers_test(p106_betas_prevax_vs_pretreatment, "p106_pretreatment_umi", "p106_prevax_umi", test = "less", correction = "BY")

[1] 100
[1] 200
[1] 300
[1] 400
[1] 500
[1] 600
[1] 700
[1] 800
[1] 900
[1] 1000
[1] 1100
[1] 1200
[1] 1300
[1] 1400
[1] 1500
[1] 1600
[1] 1700
[1] 1800
[1] 1900
[1] 2000
[1] 2100
[1] 2200
[1] 2300
[1] 2400
[1] 2500
[1] 2600
[1] 2700
[1] 2800
[1] 2900
[1] 3000
[1] 3100
[1] 3200
[1] 3300
[1] 3400
[1] 3500
[1] 3600
[1] 3700
[1] 3800
[1] 3900
[1] 4000
[1] 4100
[1] 4200
[1] 4300
[1] 4400
[1] 4500
[1] 4600
[1] 4700
[1] 4800
[1] 4900
[1] 5000
[1] 5100
[1] 5200
[1] 5300
[1] 5400
[1] 5500
[1] 5600
[1] 5700
[1] 5800
[1] 5900
[1] 6000
[1] 6100
[1] 6200
[1] 6300
[1] 6400
[1] 6500
[1] 6600
[1] 6700
[1] 6800
[1] 6900
[1] 7000
[1] 7100
[1] 7200
[1] 7300
[1] 7400
[1] 7500
[1] 7600
[1] 7700
[1] 7800
[1] 7900
[1] 8000
[1] 8100
[1] 8200
[1] 8300
[1] 8400
[1] 8500
[1] 8600
[1] 8700
[1] 8800
[1] 8900
[1] 9000
[1] 9100
[1] 9200
[1] 9300
[1] 9400
[1] 9500
[1] 9600
[1] 9700
[1] 9800
[1] 9900
[1] 10000
[1] 10100
[1] 10200
[1] 10300
[1] 10400
[1] 10500
[1] 10600
[1] 10700
[1] 10800
[1] 10900
[1] 11000
[1] 11100
[1] 11200
[1] 11300
[1] 11400
[1] 11500
[1] 11600
[1] 11700
[1] 11800
[1] 11900
[1] 12000
[1] 12100
[1] 12200
[1] 12300
[1] 12400
[1] 12500
[1] 12600
[1] 12700
[1] 12800
[1] 12900
[1] 13000
[1] 13100
[1] 13200
[1] 13300
[1] 13400
[1] 13500
[1] 13600
[1] 13700
[1] 13800
[1] 13900
[1] 14000
[1] 14100
[1] 14200
[1] 14300
[1] 14400
[1] 14500
[1] 14600
[1] 14700
[1] 14800
[1] 14900
[1] 15000
[1] 15100
[1] 15200
[1] 15300
[1] 15400
[1] 15500
[1] 15600
[1] 15700
[1] 15800
[1] 15900
[1] 16000
[1] 16100
[1] 16200
[1] 16300
[1] 16400
[1] 16500
[1] 16600
[1] 16700
[1] 16800
[1] 16900
[1] 17000
[1] 17100
[1] 17200
[1] 17300
[1] 17400
[1] 17500
[1] 17600
[1] 17700
[1] 17800
[1] 17900
[1] 18000
[1] 18100
[1] 18200
[1] 18300
[1] 18400
[1] 18500
[1] 18600
[1] 18700
[1] 18800
[1] 18900
[1] 19000
[1] 19100
[1] 19200
[1] 19300
[1] 19400
[1] 19500
[1] 19600
[1] 19700
[1] 19800
[1] 19900
[1] 20000
[1] 20100
[1] 20200
[1] 20300
[1] 20400
[1] 20500
[1] 20600
[1] 20700
[1] 20800
[1] 20900
[1] 21000
[1] 21100
[1] 21200
[1] 21300
[1] 21400
[1] 21500
[1] 21600
[1] 21700
[1] 21800
[1] 21900
[1] 22000
[1] 22100
[1] 22200
[1] 22300
[1] 22400
[1] 22500
[1] 22600
[1] 22700
[1] 22800
[1] 22900
[1] 23000
[1] 23100
[1] 23200
[1] 23300
[1] 23400
[1] 23500
[1] 23600
[1] 23700
[1] 23800
[1] 23900
[1] 24000
[1] 24100
[1] 24200
[1] 24300
[1] 24400
[1] 24500
[1] 24600
[1] 24700

p108_pval_prevax_vs_pretreatment <- run_fishers_test(p108_betas_prevax_vs_pretreatment, "p108_pretreatment_umi", "p108_prevax_umi", test = "less", correction = "BY")

[1] 100
[1] 200
[1] 300
[1] 400
[1] 500
[1] 600
[1] 700
[1] 800
[1] 900
[1] 1000
[1] 1100
[1] 1200
[1] 1300
[1] 1400
[1] 1500
[1] 1600
[1] 1700
[1] 1800
[1] 1900
[1] 2000
[1] 2100
[1] 2200
[1] 2300
[1] 2400
[1] 2500
[1] 2600
[1] 2700
[1] 2800
[1] 2900
[1] 3000
[1] 3100
[1] 3200
[1] 3300
[1] 3400
[1] 3500
[1] 3600
[1] 3700
[1] 3800
[1] 3900
[1] 4000
[1] 4100
[1] 4200
[1] 4300
[1] 4400
[1] 4500
[1] 4600
[1] 4700
[1] 4800
[1] 4900
[1] 5000
[1] 5100
[1] 5200
[1] 5300
[1] 5400
[1] 5500
[1] 5600
[1] 5700
[1] 5800
[1] 5900
[1] 6000
[1] 6100
[1] 6200
[1] 6300
[1] 6400
[1] 6500
[1] 6600
[1] 6700
[1] 6800
[1] 6900
[1] 7000
[1] 7100
[1] 7200
[1] 7300
[1] 7400
[1] 7500
[1] 7600
[1] 7700
[1] 7800
[1] 7900
[1] 8000
[1] 8100
[1] 8200
[1] 8300
[1] 8400
[1] 8500
[1] 8600
[1] 8700
[1] 8800
[1] 8900
[1] 9000
[1] 9100
[1] 9200
[1] 9300
[1] 9400
[1] 9500
[1] 9600
[1] 9700
[1] 9800
[1] 9900
[1] 10000
[1] 10100
[1] 10200
[1] 10300
[1] 10400
[1] 10500
[1] 10600
[1] 10700
[1] 10800
[1] 10900
[1] 11000
[1] 11100
[1] 11200
[1] 11300
[1] 11400
[1] 11500
[1] 11600
[1] 11700
[1] 11800
[1] 11900
[1] 12000
[1] 12100
[1] 12200
[1] 12300
[1] 12400
[1] 12500
[1] 12600
[1] 12700
[1] 12800
[1] 12900
[1] 13000
[1] 13100
[1] 13200
[1] 13300
[1] 13400
[1] 13500
[1] 13600
[1] 13700
[1] 13800
[1] 13900
[1] 14000
[1] 14100
[1] 14200
[1] 14300
[1] 14400
[1] 14500
[1] 14600
[1] 14700
[1] 14800
[1] 14900
[1] 15000
[1] 15100
[1] 15200
[1] 15300
[1] 15400
[1] 15500
[1] 15600
[1] 15700
[1] 15800
[1] 15900
[1] 16000
[1] 16100
[1] 16200
[1] 16300
[1] 16400
[1] 16500
[1] 16600
[1] 16700
[1] 16800
[1] 16900
[1] 17000
[1] 17100
[1] 17200
[1] 17300
[1] 17400
[1] 17500
[1] 17600
[1] 17700
[1] 17800
[1] 17900
[1] 18000
[1] 18100
[1] 18200
[1] 18300
[1] 18400
[1] 18500
[1] 18600
[1] 18700
[1] 18800
[1] 18900
[1] 19000
[1] 19100
[1] 19200
[1] 19300
[1] 19400
[1] 19500
[1] 19600
[1] 19700
[1] 19800
[1] 19900
[1] 20000
[1] 20100
[1] 20200
[1] 20300
[1] 20400
[1] 20500
[1] 20600
[1] 20700
[1] 20800
[1] 20900
[1] 21000
[1] 21100
[1] 21200
[1] 21300
[1] 21400
[1] 21500
[1] 21600
[1] 21700
[1] 21800
[1] 21900
[1] 22000
[1] 22100
[1] 22200
[1] 22300
[1] 22400
[1] 22500
[1] 22600
[1] 22700
[1] 22800
[1] 22900
[1] 23000
[1] 23100
[1] 23200
[1] 23300
[1] 23400
[1] 23500
[1] 23600
[1] 23700
[1] 23800
[1] 23900
[1] 24000
[1] 24100
[1] 24200
[1] 24300
[1] 24400
[1] 24500
[1] 24600
[1] 24700
[1] 24800
[1] 24900
[1] 25000
[1] 25100
[1] 25200
[1] 25300
[1] 25400
[1] 25500
[1] 25600
[1] 25700
[1] 25800
[1] 25900
[1] 26000
[1] 26100
[1] 26200
[1] 26300
[1] 26400
[1] 26500
[1] 26600
[1] 26700
[1] 26800
[1] 26900
[1] 27000
[1] 27100
[1] 27200
[1] 27300
[1] 27400
[1] 27500
[1] 27600
[1] 27700
[1] 27800
[1] 27900
[1] 28000

p109_pval_prevax_vs_pretreatment <- run_fishers_test(p109_betas_prevax_vs_pretreatment, "p109_pretreatment_umi", "p109_prevax_umi", test = "less", correction = "BY")

[1] 100
[1] 200
[1] 300
[1] 400
[1] 500
[1] 600
[1] 700
[1] 800
[1] 900
[1] 1000
[1] 1100
[1] 1200
[1] 1300
[1] 1400
[1] 1500
[1] 1600
[1] 1700
[1] 1800
[1] 1900
[1] 2000
[1] 2100
[1] 2200
[1] 2300
[1] 2400
[1] 2500
[1] 2600
[1] 2700
[1] 2800
[1] 2900
[1] 3000
[1] 3100
[1] 3200
[1] 3300
[1] 3400
[1] 3500
[1] 3600
[1] 3700
[1] 3800
[1] 3900
[1] 4000
[1] 4100
[1] 4200
[1] 4300
[1] 4400
[1] 4500
[1] 4600
[1] 4700
[1] 4800
[1] 4900
[1] 5000
[1] 5100
[1] 5200
[1] 5300
[1] 5400
[1] 5500
[1] 5600
[1] 5700
[1] 5800
[1] 5900
[1] 6000
[1] 6100
[1] 6200
[1] 6300
[1] 6400
[1] 6500
[1] 6600
[1] 6700
[1] 6800
[1] 6900
[1] 7000
[1] 7100
[1] 7200
[1] 7300
[1] 7400
[1] 7500
[1] 7600
[1] 7700
[1] 7800
[1] 7900
[1] 8000
[1] 8100
[1] 8200
[1] 8300
[1] 8400
[1] 8500
[1] 8600
[1] 8700
[1] 8800
[1] 8900
[1] 9000
[1] 9100
[1] 9200
[1] 9300
[1] 9400
[1] 9500
[1] 9600
[1] 9700
[1] 9800
[1] 9900
[1] 10000
[1] 10100
[1] 10200
[1] 10300
[1] 10400
[1] 10500
[1] 10600
[1] 10700
[1] 10800
[1] 10900
[1] 11000
[1] 11100
[1] 11200
[1] 11300
[1] 11400
[1] 11500
[1] 11600
[1] 11700
[1] 11800
[1] 11900
[1] 12000
[1] 12100
[1] 12200
[1] 12300
[1] 12400
[1] 12500
[1] 12600
[1] 12700
[1] 12800
[1] 12900

p110_pval_prevax_vs_pretreatment <- run_fishers_test(p110_betas_prevax_vs_pretreatment, "p110_pretreatment_umi", "p110_prevax_umi", test = "less", correction = "BY")

[1] 100
[1] 200
[1] 300
[1] 400
[1] 500
[1] 600
[1] 700
[1] 800
[1] 900
[1] 1000
[1] 1100
[1] 1200
[1] 1300
[1] 1400
[1] 1500
[1] 1600
[1] 1700
[1] 1800
[1] 1900
[1] 2000
[1] 2100
[1] 2200
[1] 2300
[1] 2400
[1] 2500
[1] 2600
[1] 2700
[1] 2800
[1] 2900
[1] 3000
[1] 3100
[1] 3200
[1] 3300
[1] 3400
[1] 3500
[1] 3600
[1] 3700
[1] 3800
[1] 3900
[1] 4000
[1] 4100
[1] 4200
[1] 4300
[1] 4400
[1] 4500
[1] 4600
[1] 4700
[1] 4800
[1] 4900
[1] 5000
[1] 5100
[1] 5200
[1] 5300
[1] 5400
[1] 5500
[1] 5600
[1] 5700
[1] 5800
[1] 5900
[1] 6000
[1] 6100
[1] 6200
[1] 6300
[1] 6400
[1] 6500
[1] 6600
[1] 6700
[1] 6800
[1] 6900
[1] 7000
[1] 7100
[1] 7200
[1] 7300
[1] 7400
[1] 7500
[1] 7600
[1] 7700
[1] 7800
[1] 7900
[1] 8000
[1] 8100
[1] 8200
[1] 8300
[1] 8400
[1] 8500
[1] 8600
[1] 8700
[1] 8800
[1] 8900
[1] 9000
[1] 9100
[1] 9200
[1] 9300
[1] 9400
[1] 9500
[1] 9600
[1] 9700
[1] 9800
[1] 9900
[1] 10000
[1] 10100
[1] 10200
[1] 10300
[1] 10400
[1] 10500
[1] 10600
[1] 10700
[1] 10800
[1] 10900
[1] 11000
[1] 11100
[1] 11200
[1] 11300
[1] 11400
[1] 11500
[1] 11600
[1] 11700
[1] 11800
[1] 11900
[1] 12000
[1] 12100
[1] 12200
[1] 12300
[1] 12400
[1] 12500
[1] 12600
[1] 12700
[1] 12800
[1] 12900
[1] 13000
[1] 13100
[1] 13200
[1] 13300
[1] 13400
[1] 13500
[1] 13600
[1] 13700
[1] 13800
[1] 13900
[1] 14000
[1] 14100
[1] 14200
[1] 14300
[1] 14400
[1] 14500
[1] 14600
[1] 14700
[1] 14800
[1] 14900
[1] 15000
[1] 15100
[1] 15200
[1] 15300
[1] 15400
[1] 15500
[1] 15600
[1] 15700
[1] 15800
[1] 15900
[1] 16000
[1] 16100
[1] 16200
[1] 16300
[1] 16400
[1] 16500
[1] 16600
[1] 16700
[1] 16800
[1] 16900
[1] 17000
[1] 17100
[1] 17200
[1] 17300
[1] 17400
[1] 17500
[1] 17600
[1] 17700
[1] 17800
[1] 17900
[1] 18000
[1] 18100
[1] 18200
[1] 18300
[1] 18400
[1] 18500
[1] 18600
[1] 18700
[1] 18800
[1] 18900
[1] 19000
[1] 19100
[1] 19200
[1] 19300
[1] 19400
[1] 19500
[1] 19600
[1] 19700
[1] 19800
[1] 19900
[1] 20000
[1] 20100
[1] 20200
[1] 20300
[1] 20400
[1] 20500
[1] 20600
[1] 20700
[1] 20800
[1] 20900
[1] 21000
[1] 21100
[1] 21200
[1] 21300
[1] 21400
[1] 21500
[1] 21600
[1] 21700
[1] 21800
[1] 21900
[1] 22000
[1] 22100
[1] 22200
[1] 22300
[1] 22400
[1] 22500
[1] 22600
[1] 22700
[1] 22800
[1] 22900
[1] 23000
[1] 23100
[1] 23200
[1] 23300
[1] 23400
[1] 23500
[1] 23600
[1] 23700
[1] 23800
[1] 23900
[1] 24000
[1] 24100
[1] 24200
[1] 24300
[1] 24400
[1] 24500
[1] 24600
[1] 24700
[1] 24800
[1] 24900
[1] 25000
[1] 25100
[1] 25200
[1] 25300
[1] 25400
[1] 25500
[1] 25600
[1] 25700
[1] 25800
[1] 25900
[1] 26000
[1] 26100
[1] 26200
[1] 26300
[1] 26400
[1] 26500
[1] 26600
[1] 26700
[1] 26800
[1] 26900
[1] 27000
[1] 27100
[1] 27200
[1] 27300
[1] 27400
[1] 27500
[1] 27600
[1] 27700
[1] 27800
[1] 27900
[1] 28000
[1] 28100
[1] 28200
[1] 28300
[1] 28400
[1] 28500
[1] 28600
[1] 28700
[1] 28800
[1] 28900
[1] 29000
[1] 29100
[1] 29200
[1] 29300
[1] 29400
[1] 29500
[1] 29600
[1] 29700
[1] 29800
[1] 29900
[1] 30000
[1] 30100
[1] 30200
[1] 30300
[1] 30400
[1] 30500
[1] 30600
[1] 30700
[1] 30800
[1] 30900
[1] 31000
[1] 31100
[1] 31200
[1] 31300
[1] 31400
[1] 31500
[1] 31600
[1] 31700
[1] 31800
[1] 31900
[1] 32000
[1] 32100
[1] 32200
[1] 32300
[1] 32400
[1] 32500
[1] 32600
[1] 32700
[1] 32800
[1] 32900
[1] 33000
[1] 33100
[1] 33200
[1] 33300
[1] 33400
[1] 33500
[1] 33600
[1] 33700
[1] 33800
[1] 33900
[1] 34000
[1] 34100
[1] 34200
[1] 34300
[1] 34400
[1] 34500
[1] 34600
[1] 34700
[1] 34800
[1] 34900
[1] 35000
[1] 35100
[1] 35200
[1] 35300
[1] 35400
[1] 35500
[1] 35600
[1] 35700
[1] 35800
[1] 35900
[1] 36000
[1] 36100
[1] 36200
[1] 36300
[1] 36400
[1] 36500
[1] 36600
[1] 36700
[1] 36800
[1] 36900
[1] 37000
[1] 37100
[1] 37200
[1] 37300
[1] 37400
[1] 37500
[1] 37600
[1] 37700
[1] 37800
[1] 37900
[1] 38000
[1] 38100
[1] 38200
[1] 38300
[1] 38400
[1] 38500
[1] 38600
[1] 38700
[1] 38800
[1] 38900
[1] 39000
[1] 39100
[1] 39200
[1] 39300
[1] 39400

p111_pval_prevax_vs_pretreatment <- run_fishers_test(p111_betas_prevax_vs_pretreatment, "p111_pretreatment_umi", "p111_prevax_umi", test = "less", correction = "BY")

[1] 100
[1] 200
[1] 300
[1] 400
[1] 500
[1] 600
[1] 700
[1] 800
[1] 900
[1] 1000
[1] 1100
[1] 1200
[1] 1300
[1] 1400
[1] 1500
[1] 1600
[1] 1700
[1] 1800
[1] 1900
[1] 2000
[1] 2100
[1] 2200
[1] 2300
[1] 2400
[1] 2500
[1] 2600
[1] 2700
[1] 2800
[1] 2900
[1] 3000
[1] 3100
[1] 3200
[1] 3300
[1] 3400
[1] 3500
[1] 3600
[1] 3700
[1] 3800
[1] 3900
[1] 4000
[1] 4100
[1] 4200
[1] 4300
[1] 4400
[1] 4500
[1] 4600
[1] 4700
[1] 4800
[1] 4900
[1] 5000
[1] 5100
[1] 5200
[1] 5300
[1] 5400
[1] 5500
[1] 5600
[1] 5700
[1] 5800
[1] 5900
[1] 6000
[1] 6100
[1] 6200
[1] 6300
[1] 6400
[1] 6500
[1] 6600
[1] 6700
[1] 6800
[1] 6900
[1] 7000
[1] 7100
[1] 7200
[1] 7300
[1] 7400
[1] 7500
[1] 7600
[1] 7700
[1] 7800
[1] 7900
[1] 8000
[1] 8100
[1] 8200
[1] 8300
[1] 8400
[1] 8500
[1] 8600
[1] 8700
[1] 8800
[1] 8900
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7.6 Count how many clones are expanded prevax compared to pretreatment

p101_pval_prevax_vs_pretreatment <- p101_pval_prevax_vs_pretreatment %>%
  mutate(Patient = "P101")
p103_pval_prevax_vs_pretreatment <- p103_pval_prevax_vs_pretreatment %>%
  mutate(Patient = "P103")
p104_pval_prevax_vs_pretreatment <- p104_pval_prevax_vs_pretreatment %>%
  mutate(Patient = "P104")
p105_pval_prevax_vs_pretreatment <- p105_pval_prevax_vs_pretreatment %>%
  mutate(Patient = "P105")
p106_pval_prevax_vs_pretreatment <- p106_pval_prevax_vs_pretreatment %>%
  mutate(Patient = "P106")
p108_pval_prevax_vs_pretreatment <- p108_pval_prevax_vs_pretreatment %>%
  mutate(Patient = "P108")
p109_pval_prevax_vs_pretreatment <- p109_pval_prevax_vs_pretreatment %>%
  mutate(Patient = "P109")
p110_pval_prevax_vs_pretreatment <- p110_pval_prevax_vs_pretreatment %>%
  mutate(Patient = "P110")
p111_pval_prevax_vs_pretreatment <- p111_pval_prevax_vs_pretreatment %>%
  mutate(Patient = "P111")

pval_prevax_vs_pretreatment <- do.call(rbind, list(p101_pval_prevax_vs_pretreatment,
                                              p103_pval_prevax_vs_pretreatment,
                                              p104_pval_prevax_vs_pretreatment,
                                              p105_pval_prevax_vs_pretreatment,
                                              p106_pval_prevax_vs_pretreatment,
                                              p108_pval_prevax_vs_pretreatment,
                                              p109_pval_prevax_vs_pretreatment,
                                              p110_pval_prevax_vs_pretreatment,
                                              p111_pval_prevax_vs_pretreatment))

7.7 Join results from the significance comparisons after nivolumab vs after vaccine

n_sig_postvax_vs_prevax <- pval_postvax_vs_prevax %>%
  filter(sig == "Sig") %>%
  dplyr::count(sig, Patient) %>%
  mutate(category = "After Vaccine")

n_sig_prevax_vs_pretreatment <- pval_prevax_vs_pretreatment %>%
  filter(sig == "Sig") %>%
  dplyr::count(sig, Patient) %>%
  mutate(category = "After Nivo")

# Get order based on vax-expanded counts
pt_order <- n_sig_postvax_vs_prevax %>%
  arrange(desc(n)) %>%
  mutate(pt_order = 1:n()) %>%
  select(Patient, pt_order)

n_sig <- rbind(n_sig_postvax_vs_prevax, n_sig_prevax_vs_pretreatment) %>%
  # Add a column for ordering
  left_join(pt_order)

Joining with `by = join_by(Patient)`

7.8 Check normal distribution assumption

t test: Paired t-test can be used only when the difference d is normally distributed. This can be checked using Shapiro-Wilk test. wilxocon: Differences between paired samples should be distributed symmetrically around the median.

diff <- n_sig %>%
  pivot_wider(id_cols = c("Patient"), values_from = "n", names_from = "category") %>%
  mutate(diff = `After Nivo` - `After Vaccine`)

med <- n_sig %>%
  pivot_wider(id_cols = c("Patient"), values_from = "n", names_from = "category") %>%
  mutate(diff = `After Nivo` - `After Vaccine`) %>%
  summarize(median = median(diff)) %>%
  pull(median)

n_sig %>%
  pivot_wider(id_cols = c("Patient"), values_from = "n", names_from = "category") %>%
  mutate(diff = `After Nivo` - `After Vaccine`) %>%
  ggplot(aes(x = diff)) +
  geom_histogram() +
  geom_vline(xintercept = -106)

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

shapiro.test(diff$diff)

    Shapiro-Wilk normality test

data:  diff$diff
W = 0.91773, p-value = 0.3737

# p val is above 0.05, can assume normality

7.9 Create Fig 3A

res <- t.test(n ~ category, data = n_sig, paired = TRUE, alternative = "two.sided")
res

    Paired t-test

data:  n by category
t = -4.4334, df = 8, p-value = 0.002187
alternative hypothesis: true mean difference is not equal to 0
95 percent confidence interval:
 -173.63357  -54.81087
sample estimates:
mean difference 
      -114.2222 

pval_df <- data.frame(
  group1 = "After Nivo",
  group2 = "After Vaccine",
  label = round(res$p.value, 5),
  y.position = 350
)

# Original  
bp <- n_sig %>%
  ggplot() +
  geom_boxplot(aes(x = category, y = n)) +
  geom_point(aes(x = category, y = n, group = Patient, color = Patient), size = 4) +
  geom_line(aes(x = category, y = n, group = Patient, color = Patient)) +
  theme_classic() +
  ylab("Number of significantly expanding clones") +
  xlab("") +
  pt_cols +
  add_pvalue(pval_df) 

bp

7.10 Save pvals

# Significantly expanded after vaccine
write.csv(p101_pval_postvax_vs_prevax,"p101_pval_postvax_vs_prevax_Part4.csv", row.names = FALSE)
write.csv(p103_pval_postvax_vs_prevax,"p103_pval_postvax_vs_prevax_Part4.csv", row.names = FALSE)
write.csv(p104_pval_postvax_vs_prevax,"p104_pval_postvax_vs_prevax_Part4.csv", row.names = FALSE)
write.csv(p105_pval_postvax_vs_prevax,"p105_pval_postvax_vs_prevax_Part4.csv", row.names = FALSE)
write.csv(p106_pval_postvax_vs_prevax,"p106_pval_postvax_vs_prevax_Part4.csv", row.names = FALSE)
write.csv(p108_pval_postvax_vs_prevax,"p108_pval_postvax_vs_prevax_Part4.csv", row.names = FALSE)
write.csv(p109_pval_postvax_vs_prevax,"p109_pval_postvax_vs_prevax_Part4.csv", row.names = FALSE)
write.csv(p110_pval_postvax_vs_prevax,"p110_pval_postvax_vs_prevax_Part4.csv", row.names = FALSE)
write.csv(p111_pval_postvax_vs_prevax,"p111_pval_postvax_vs_prevax_Part4.csv", row.names = FALSE)

# Significantly expanded after Nivo
write.csv(p101_pval_prevax_vs_pretreatment,"p101_pval_prevax_vs_pretreatment_Part4.csv", row.names = FALSE)
write.csv(p103_pval_prevax_vs_pretreatment,"p103_pval_prevax_vs_pretreatment_Part4.csv", row.names = FALSE)
write.csv(p104_pval_prevax_vs_pretreatment,"p104_pval_prevax_vs_pretreatment_Part4.csv", row.names = FALSE)
write.csv(p105_pval_prevax_vs_pretreatment,"p105_pval_prevax_vs_pretreatment_Part4.csv", row.names = FALSE)
write.csv(p106_pval_prevax_vs_pretreatment,"p106_pval_prevax_vs_pretreatment_Part4.csv", row.names = FALSE)
write.csv(p108_pval_prevax_vs_pretreatment,"p108_pval_prevax_vs_pretreatment_Part4.csv", row.names = FALSE)
write.csv(p109_pval_prevax_vs_pretreatment,"p109_pval_prevax_vs_pretreatment_Part4.csv", row.names = FALSE)
write.csv(p110_pval_prevax_vs_pretreatment,"p110_pval_prevax_vs_pretreatment_Part4.csv", row.names = FALSE)
write.csv(p111_pval_prevax_vs_pretreatment,"p111_pval_prevax_vs_pretreatment_Part4.csv", row.names = FALSE)

7.11 Get session info

sessionInfo()

R version 4.3.2 (2023-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Rocky Linux 8.10 (Green Obsidian)

Matrix products: default
BLAS/LAPACK: /usr/lib64/libopenblasp-r0.3.15.so;  LAPACK version 3.9.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

time zone: America/New_York
tzcode source: system (glibc)

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] ggprism_1.0.5   patchwork_1.3.0 lubridate_1.9.4 forcats_1.0.0  
 [5] stringr_1.5.1   purrr_1.0.4     readr_2.1.5     tidyr_1.3.1    
 [9] tibble_3.2.1    ggplot2_3.5.1   tidyverse_2.0.0 dplyr_1.1.4    

loaded via a namespace (and not attached):
 [1] gtable_0.3.6      jsonlite_1.8.9    compiler_4.3.2    tidyselect_1.2.1 
 [5] scales_1.3.0      fastmap_1.2.0     R6_2.6.1          labeling_0.4.3   
 [9] generics_0.1.3    knitr_1.49        htmlwidgets_1.6.4 munsell_0.5.1    
[13] pillar_1.10.1     tzdb_0.5.0        rlang_1.1.5       stringi_1.8.4    
[17] xfun_0.50         timechange_0.3.0  cli_3.6.3         withr_3.0.2      
[21] magrittr_2.0.3    digest_0.6.37     grid_4.3.2        rstudioapi_0.17.1
[25] hms_1.1.3         lifecycle_1.0.4   vctrs_0.6.5       evaluate_1.0.1   
[29] glue_1.8.0        farver_2.1.2      colorspace_2.1-1  rmarkdown_2.29   
[33] tools_4.3.2       pkgconfig_2.0.3   htmltools_0.5.8.1